{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Weight Initialization\n",
    "In this lesson, you'll learn how to find good initial weights for a neural network. Having good initial weights can place the neural network close to the optimal solution. This allows the neural network to come to the best solution quicker. \n",
    "\n",
    "## Testing Weights\n",
    "### Dataset\n",
    "To see how different weights perform, we'll test on the same dataset and neural network. Let's go over the dataset and neural network.\n",
    "\n",
    "We'll be using the [MNIST dataset](https://en.wikipedia.org/wiki/MNIST_database) to demonstrate the different initial weights. As a reminder, the MNIST dataset contains images of handwritten numbers, 0-9, with normalized input (0.0 - 1.0).  Run the cell below to download and load the MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting MNIST Dataset...\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Data Extracted.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "import helper\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "print('Getting MNIST Dataset...')\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "print('Data Extracted.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network\n",
    "<img style=\"float: left\" src=\"images/neural_network.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784)\n",
      "(10000, 784)\n",
      "(5000, 784)\n",
      "Datasets(train=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x109dbeba8>, validation=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x109d109e8>, test=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x10febcb38>)\n"
     ]
    }
   ],
   "source": [
    "print(mnist.train.images.shape)\n",
    "print(mnist.test.images.shape)\n",
    "print(mnist.validation.images.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the neural network, we'll test on a 3 layer neural network with ReLU activations and an Adam optimizer.  The lessons you learn apply to other neural networks, including different activations and optimizers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784)\n"
     ]
    }
   ],
   "source": [
    "# Save the shapes of weights for each layer\n",
    "layer_1_weight_shape = (mnist.train.images.shape[1], 256)\n",
    "layer_2_weight_shape = (256, 128)\n",
    "layer_3_weight_shape = (128, mnist.train.labels.shape[1])\n",
    "print(mnist.train.images.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Weights\n",
    "Let's start looking at some initial weights.\n",
    "### All Zeros or Ones\n",
    "If you follow the principle of [Occam's razor](https://en.wikipedia.org/wiki/Occam's_razor), you might think setting all the weights to 0 or 1 would be the best solution.  This is not the case.\n",
    "\n",
    "With every weight the same, all the neurons at each layer are producing the same output.  This makes it hard to decide which weights to adjust.\n",
    "\n",
    "Let's compare the loss with all ones and all zero weights using `helper.compare_init_weights`.  This function will run two different initial weights on the neural network above for 2 epochs.  It will plot the loss for the first 100 batches and print out stats after the 2 epochs (~860 batches). We plot the first 100 batches to better judge which weights performed better at the start.\n",
    "\n",
    "Run the cell below to see the difference between weights of all zeros against all ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化 Weights\n",
    "\n",
    "### 全0或者全1\n",
    "\n",
    "如果你遵循 Occam's razor 规则，你可能会想把weights 设置为全0或者全1是最好的解决方案。不过不是这样的\n",
    "\n",
    "每个weight一样，每一层的所有神经元会得出相同的输出。这让决定优化哪个weight变得很困难。\n",
    "\n",
    "让我们用 `helper.compare_init_weights` 对比下全1和全0情况下的loss（损失）。它会在神经网络上运行两个不同的初始化weights。然后画出前100个batches的loss，并打印出统计信息。我们打印出前100个batches来判断那个weights变相的更好。\n",
    "\n",
    "运行下面cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAEWCAYAAACkI6QfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niCgB/H9uDL4T3hrDaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ddbe128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   11.260% -- All Zeros\n",
      "   10.700% -- All Ones\n",
      "Loss\n",
      "    2.310  -- All Zeros\n",
      "  185.515  -- All Ones\n"
     ]
    }
   ],
   "source": [
    "all_zero_weights = [\n",
    "    tf.Variable(tf.zeros(layer_1_weight_shape)),\n",
    "    tf.Variable(tf.zeros(layer_2_weight_shape)),\n",
    "    tf.Variable(tf.zeros(layer_3_weight_shape))\n",
    "]\n",
    "\n",
    "all_one_weights = [\n",
    "    tf.Variable(tf.ones(layer_1_weight_shape)),\n",
    "    tf.Variable(tf.ones(layer_2_weight_shape)),\n",
    "    tf.Variable(tf.ones(layer_3_weight_shape))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'All Zeros vs All Ones',\n",
    "    [\n",
    "        (all_zero_weights, 'All Zeros'),\n",
    "        (all_one_weights, 'All Ones')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the accuracy is close to guessing for both zeros and ones, around 10%.\n",
    "\n",
    "The neural network is having a hard time determining which weights need to be changed, since the neurons have the same output for each layer.  To avoid neurons with the same output, let's use unique weights.  We can also randomly select these weights to avoid being stuck in a local minimum for each run.\n",
    "\n",
    "A good solution for getting these random weights is to sample from a uniform distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如你所见，精度差不多，在10%左右\n",
    "\n",
    "神经网络很难查明哪个weights需要被修改，因为所有的neurons拥有相同的输出。为了避免neurons拥有相同的输出，让我们使用唯一的weights。我们可以随机选择weights来避免陷入本地最优解\n",
    "\n",
    "获取随机weights的一个好办法是从均匀分布中抽取样本。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform Distribution\n",
    "A [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous%29) has the equal probability of picking any number from a set of numbers. We'll be picking from a continous distribution, so the chance of picking the same number is low. We'll use TensorFlow's `tf.random_uniform` function to pick random numbers from a uniform distribution.\n",
    "\n",
    ">#### [`tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)`](https://www.tensorflow.org/api_docs/python/tf/random_uniform)\n",
    ">Outputs random values from a uniform distribution.\n",
    "\n",
    ">The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded.\n",
    "\n",
    ">- **shape:** A 1-D integer Tensor or Python array. The shape of the output tensor.\n",
    "- **minval:** A 0-D Tensor or Python value of type dtype. The lower bound on the range of random values to generate. Defaults to 0.\n",
    "- **maxval:** A 0-D Tensor or Python value of type dtype. The upper bound on the range of random values to generate. Defaults to 1 if dtype is floating point.\n",
    "- **dtype:** The type of the output: float32, float64, int32, or int64.\n",
    "- **seed:** A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.\n",
    "- **name:** A name for the operation (optional).\n",
    "\n",
    "We can visualize the uniform distribution by using a histogram. Let's map the values from `tf.random_uniform([1000], -3, 3)` to a histogram using the `helper.hist_dist` function. This will be `1000` random float values from `-3` to `3`, excluding the value `3`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform distribution 均匀分布\n",
    "\n",
    "均匀分布从一组数字中挑选任意数字的概率一样。我们从连续的分布中选择数据，所以选中相同数字的几率就很小。我们使用 Tensorflow 的 `tf.random_uniform` 方法从一个均匀分布中选择随机数字。\n",
    "\n",
    "我们可以使用直方图画出均匀分布。让我们将 `tf.random_uniform([1000], -3, 3)` 生成的数据映射到直方图中，通过 `helper.hist_dist` 方法。这是1000个在-3到3之间的随机数据，包含3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEICAYAAAB/Dx7IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3JLUmP8PdfyQ50/t+nwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122304fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.hist_dist('Random Uniform (minval=-3, maxval=3)', tf.random_uniform([1000], -3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram used 500 buckets for the 1000 values.  Since the chance for any single bucket is the same, there should be around 2 values for each bucket. That's exactly what we see with the histogram.  Some buckets have more and some have less, but they trend around 2.\n",
    "\n",
    "Now that you understand the `tf.random_uniform` function, let's apply it to some initial weights.\n",
    "\n",
    "### Baseline\n",
    "\n",
    "\n",
    "Let's see how well the neural network trains using the default values for `tf.random_uniform`, where `minval=0.0` and `minval=1.0`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直方图使用500个柱子表示1000个数。由于任何一个柱的几率是相同的，所以每个柱大约有2个值。正如我们在直方图看到的。\n",
    "有些柱多有些柱少，但都应该靠近2.\n",
    "\n",
    "## 底线\n",
    "\n",
    "让我们看看神经网络使用 `tf.random_uniform` 默认值（值在0 1）的训练结果会有多好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAEWCAYAAACKUPh8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4VdX59vHvkwTCjIwRAggKDgwiEgmgFS0OIFVpHYpv\nVX4VpzpX31qHvh1+1qqtrZa2WiyOda5aRRypKM5AFJmnoMg8j8qY5Hn/WDvlEIMEkpOdnHN/rivX\n2Wedvfd5VmrhZu219zJ3R0RERCQdZcRdgIiIiEhcFIREREQkbSkIiYiISNpSEBIREZG0pSAkIiIi\naUtBSERERNKWgpBIDWVm/2Nm7ye8/8rMDo6zJhGRVKMgJFJBZrbQzLZGgWS9mb1iZu2r6/vdvZG7\nf15d3ycikg4UhET2zenu3ghoA6wE/hJzPSIiUgkKQiL7wd23Ac8BXQHMbIiZTTGzTWa22Mx+Xbqv\nmdUzs8fNbK2ZbTCzyWaWE33W1MweNLPlZrbUzH5rZpnlfaeZuZl1jrYfMbO/RaNSm81sopkdkrDv\n4WY2zszWmdlcMzs3ib8OEZFaS0FIZD+YWQPgh8DHUdPXwIXAAcAQ4CdmNjT6bDjQFGgPtAAuB7ZG\nnz0CFAGdgV7AKcDFFSxjGPAboBlQCNwe1dYQGAc8CbSO9rvPzLrue09FRFKbgpDIvnnRzDYAG4GT\ngT8AuPs77j7d3UvcfRrwFDAgOmYnIQB1dvdid//E3TdFo0KnAde5+9fuvgq4hxBcKuLf7j7J3YuA\nJ4CjovbvAQvd/WF3L3L3KcDzwDmV7r2ISIrJirsAkVpmqLv/J7p8dSYwIRppOQi4E+gO1AWygX9F\nx/yTMBr0tJkdADwO3BodUwdYbmal588AFlewlhUJ21uARtH2QUB+FNhKZUV1iIhIAo0IieyHaGTn\nBaAYOI5wGWoM0N7dmwJ/Byzad6e7/8bduwL9CSM2FxICz3agpbsfEP00cfdulSxvMTAh4ZwHRHec\n/aSS5xURSTkKQiL7wYIzCfNzZgONgXXuvs3M+gD/J2HfE82sRzSKtIlwqazE3ZcDbwJ/NLMmZpZh\nZoeY2YBvfuM+GQscamYXmFmd6OcYMzuikucVEUk5CkIi++ZlM/uKEGhuB4a7+0zgCuB/zWwz8Evg\n2YRjDiTcYbaJEJomsOsy1YWES2mzgPXRfm0qU6C7byZMuh4GLCNcQruLcLlOREQSmLvHXYOIiIhI\nLDQiJCIiImlLQUhERETSloKQiIiIpC0FIREREUlbafdAxZYtW3rHjh3jLkNEpFb55JNP1rh7q0qe\no3VWVtZowoNH9Q9xqQ4lwIyioqKLe/fuvaq8HdIuCHXs2JGCgoK4yxARqVXM7MvKniMrK2v0gQce\neESrVq3WZ2Rk6JZlSbqSkhJbvXp11xUrVowGzihvHyVyERGpLt1btWq1SSFIqktGRoa3atVqI2EU\nsvx9qrEeERFJbxkKQVLdov/m9ph3FIREREQkbSU1CJnZQjObbmafmVlB1NbczMaZ2fzotVnC/jeb\nWaGZzTWzUxPae0fnKTSzkRYt1W1m2Wb2TNQ+0cw6JrM/IiIiklqqY0ToRHc/yt3zovc3AW+5exfg\nreg9ZtaVsDZSN2AQcF+0SCXA/cAlQJfoZ1DUPgJY7+6dgXsI6ymJiIh8w5o1azLvvPPO3e58u+yy\ny9p17ty522WXXdYumd89d+7cul26dOmWzO/YVwMGDOi8Zs2aTIDf/va3rQ8++OBuZ5xxRqdkfudZ\nZ53VMTc3t8fvf//7VgBbt261IUOGHNyhQ4fuRx555OFz586tu7dzXH311bkHHnjgkQ0aNOiV2P67\n3/2u1b333ttiX2uK49LYmcCj0fajwNCE9qfdfbu7fwEUAn3MrA3QxN0/9rAw2mNljik913PAwNLR\nIhERkURr167NfPDBB1sntj355JMt58yZM3PUqFFL9nTczp07k19cDCZMmFDYsmXLYoAHH3yw1bhx\n4+aNGTPmi4ocW5nfyW9/+9slN95442qAP//5zy2bNm1atGjRohlXXXXVyuuvv36vgXTo0KEbJk6c\nOLts+9VXX7121KhROftaT7Jvn3fgP2ZWDIxy9weAHHdfHn2+AigtOhf4OOHYJVHbzmi7bHvpMYsB\n3L3IzDYCLYA1iUWY2aXApQAdOnSomp6JiMj+u+ii9syY0aBKz9m9+xYeemjxnj6+4YYb2i1evDj7\n8MMP7zpgwIBN8+fPr7dly5bM7t27d73hhhuWX3LJJetL9x05cmSLF198sdmWLVsyiouL7T//+c/8\nQYMGdd64cWNmUVGR/fKXv1x2/vnnb5g7d27dwYMHd+nTp89XBQUFjXJycna88cYbhY0aNfL33nuv\nwcUXX9wR4IQTTthUeu4tW7bYhRdeeNC0adMaZGZm8vvf/37x6aefvnnkyJEtxowZc8CWLVsyvvzy\ny3pXXnnlih07dmQ888wzLerWrVvy5ptvzs/JySkur299+vQ57O677158/PHHb1m+fHlWXl7eEUuX\nLp0+cuTIFmPHjj1g69atGYsWLcoePHjwhr///e9LAHJzc3sUFBTMvuGGG9ouWbIke/DgwV1+9KMf\nrbn88svX/uhHP+q4aNGi7Pr165c88MADX+bn52+9/vrr237++efZixYtys7Nzd1+8sknb9rfekuN\nHTv2gF//+tfLAH784x+v//nPf96hpKSEjIw9j9MMHDjw6/LaGzduXNKuXbvtb7/9doMTTzxxy7d9\nb6Jkjwgd5+5HAYOBK83s+MQPoxGepN9B4O4PuHueu+e1arWfzwObMQNuvhlcNzyIiNRGf/zjH5e0\nb99++5w5c2aNGjVqyfjx4wuzs7NL5syZMysxBJWaOXNmg5deemnB5MmT5zZo0KDklVdeKZw1a9bs\nCRMmzLvlllvalZSUALBo0aJ611xzzarCwsKZTZs2LX7ssceaAYwYMaLjvffeu2ju3LmzEs971113\ntTYz5s2bN+vJJ5/8/NJLL+24ZcsWA5g3b179V155ZcHkyZNn33HHHbkNGjQomT179qy8vLyvR40a\ntc+XfQBmzZrV4MUXX/x89uzZM8eMGdOssLCwTuLnTz755KLWrVvvnDBhwrxf/epXq2688ca2PXv2\n3DJv3rxZt91229Lhw4f/93LZ/Pnz67377rtzX3755S+qot6VK1fW7dSp0w6AOnXq0KhRo+KVK1fu\n9yDN0Ucf/fU777zTeF+OSeqIkLsvjV5Xmdm/gT7ASjNr4+7Lo8tepU96XAq0Tzi8XdS2NNou2554\nzBIzywKaAmuT0pnx4+HOO+Hoo+Gcc5LyFSIiaeNbRm5qiu985zubSkc0SkpK7Lrrrmv38ccfN8rI\nyGDVqlV1lyxZkgWQm5u7vX///lsBevXqtWXhwoXZa9asydy8eXPm4MGDvwK46KKL1o4fP74pwIcf\nftjo6quvXhXtv61t27Y7pk+fXg+gf//+m5s1a1bSrFmzkkaNGhWfc845GwB69OixZdq0afs1gnbc\nccdtatGiRTFA586dty1YsCC7c+fOe7y2NWnSpMbPP/98IcAZZ5yx+dJLL81at25dBsCgQYM2NGrU\n6L8jAsmotzJat25dNGfOnHr7ckzSRoTMrKGZNS7dBk4BZgBjgOHRbsOBl6LtMcCw6E6wToRJ0ZOi\ny2ibzKxvNP/nwjLHlJ7rbGB8NMpU9a68MoSga6+FjRuT8hUiIlJzNGjQoKR0e9SoUc3Xrl2bNX36\n9Nlz5syZ1aJFi51bt27NAKhbt+5//97JzMz0oqKi/Z6rmniujIwM6tWr56Xb33berKwsLy4OV6FK\nR5fKO2dmZqbv3Llzv+tr2LBhSeL7/a23VE5Ozo4vvviiLoR5R1999VVmTk5O0f7Wt23btoz69euX\n7H3PXZJ5aSwHeN/MpgKTgFfc/XXgTuBkM5sPnBS9x91nAs8Cs4DXgSvdvfTa4hXAaMIE6gXAa1H7\ng0ALMysErie6Ay0pMjNh1ChYsQJ+8YukfY2IiCRH06ZNi7/++uv9+ntv48aNmS1bttyZnZ3tL7/8\ncuNly5Z9691NLVu2LG7cuHHxG2+80QjgkUceaV762bHHHvvV448/3hxg2rRp2cuXL6975JFHbtuf\nukq1b99++6RJkxoCPPHEE832tv+3yc/P3/zwww+3ABg7dmzjZs2aFTVv3nyfwkVFDRkyZMNDDz3U\nAuDhhx9u1q9fv82l84M6deq0z3fZzZs3L7t79+5b9+WYpAUhd//c3XtGP93c/faofa27D3T3Lu5+\nkruvSzjmdnc/xN0Pc/fXEtoL3L179NlVpaM+7r7N3c9x987u3sfdP09WfwDIywsjQ3/7G2i9MhGR\nWuXAAw8s7t2791ddunQp93b5J554oul1113XtrxjL7744nVTp05teOihh3Z99NFHW3Tq1GmvweXB\nBx9ceM0113Q4/PDDu7r7f0dHbrzxxlUlJSV26KGHdv3hD394yKhRoxbWr1+/UlczbrrpppUPPvhg\nqyOOOKLrmjVrKjXt5a677lo2ZcqUBoceemjXW2+9NfeRRx6p0J1k++Paa69ds379+qwOHTp0/8tf\n/nLg3XffvQRg+fLlWYm/s0SXX355u5ycnCO3bduWkZOTc+T111//3//NJk+e3OiMM87YVN5xe2LJ\nupJUU+Xl5XmlFl3duBGOOALatIFJk8JIkYhIijOzTxKeB7dfpk6durBnz55r9r6npKqzzjqr4/e+\n972NP/7xj78xOT3RU0891XTBggXZv/jFL8pdMb48H3zwQf0//OEPB7744ovfCG5Tp05t2bNnz47l\nHZd2q89XWtOmcNttcPHFMGVKGCUSERGRvWrSpEnxbbfd1nb16tVZpc8SKs955523z5NxV61aVeeu\nu+5auvc9d6cgtD9OOSW8fvSRgpCIiFSbCy64oMPkyZMbJbb95Cc/WXnttdcm547pKvbwww8n7W7B\n73//+/t0SayUgtD+aN8ecnNDELr66rirERGpLUpKSkpMK9Dvv3/+85+L4q6htikpKTFgj5O9tfr8\n/urfPwQhERGpqBmrV69uGv3FJJJ0JSUltnr16qaEx/eUSyNC+6tfP/jXv2D58jBxWkREvlVRUdHF\nK1asGL1ixYru6B/iUj1KgBlFRUUX72kHBaH91a9feP3oI/jBD+KtRUSkFujdu/cq4Iy46xBJpES+\nv3r1guxs+PDDuCsRERGR/aQgtL+ys6F3b80TEhERqcUUhCqjf//whOnt27/52erV0K2bnkAtIiJS\ngykIVUa/frBjR3iwYlnvvguzZsELL1R/XSIiIlIhCkKVUTphurx5QqUjQe+/X331iIiIyD5REKqM\nNm2gY8fy5wmVBqHJk8u/dCYiIiKxUxCqrH79wohQ4uK17iEItW0L27bBp5/GV5+IiIjskYJQZfXr\nB8uWwaKEp54vWAAbNsCVV4b3ujwmIiJSIykIVdbAgeH11Vd3tU2eHF6HDIEuXRSEREREaigFoco6\n4gjo3BleemlXW0EB1KsHXbvCccfBBx/sfulMREREagQFocoyg6FDYfx42LgxtE2eHJ48XacOHHss\nrF0Lc+fGW6eIiIh8g4JQVRg6FHbuhNdeg+LiMDk6Ly98dtxx4VWXx0RERGocBaGq0LcvtG4dLo/N\nnQtff70rCB16KLRsqSAkIiJSAykIVYXMTDj99DBh+oMPQtsxx4RXszAqpCAkIiJS4ygIVZWhQ2HT\nJvjTn6BRozASVOrYY8Mt9StWxFefiIiIfIOCUFUZOBAaNoQ5c8Kq9JmZuz4rnSf07rvx1CYiIiLl\nUhCqKvXrw6mnhu3S+UGleveG3Fy4//7qr0tERET2SEGoKg0dGl7LBqE6deCGG+Cdd8pfl0xERERi\noSBUlc49F+65Z1cgSnTJJdCiBdxxR/XXJSIiIuVSEKpK2dlw3XXhqdJlNWoE11wDL78M06dXf20i\nIiLyDQpC1emqq0IguvPOuCsRERERFISqV/PmcPnl8PTT4XZ6ERERiZWCUHW7/vowefquu+KuRERE\nJO0pCFW3Nm1gxAh45BFYvDjuakRERNJa0oOQmWWa2RQzGxu9b25m48xsfvTaLGHfm82s0Mzmmtmp\nCe29zWx69NlIM7OoPdvMnonaJ5pZx2T3p0rceCO4wx/+EHclIiIiaa06RoSuBWYnvL8JeMvduwBv\nRe8xs67AMKAbMAi4z8xKH898P3AJ0CX6GRS1jwDWu3tn4B6gdlxvOugguPBC+Mc/tOyGiIhIjJIa\nhMysHTAEGJ3QfCbwaLT9KDA0of1pd9/u7l8AhUAfM2sDNHH3j93dgcfKHFN6rueAgaWjRTXezTfD\njh3wxz/GXYmIiEjaSvaI0L3AjUBJQluOuy+PtlcAOdF2LpA4aWZJ1JYbbZdt3+0Ydy8CNgItyhZh\nZpeaWYGZFaxevbpSHaoynTvDeeeFZTfWrIm7GhERkbSUtCBkZt8DVrn7J3vaJxrh8WTVkPA9D7h7\nnrvntWrVKtlfV3G33AJbtsB998VdiYiISFpK5ojQscAZZrYQeBr4rpk9DqyMLncRva6K9l8KtE84\nvl3UtjTaLtu+2zFmlgU0BdYmozNJ0bUr9OsHY8fGXYmIiEhaSloQcveb3b2du3ckTIIe7+7nA2OA\n4dFuw4GXou0xwLDoTrBOhEnRk6LLaJvMrG80/+fCMseUnuvs6DuSPsJUpU45BQoKYN26uCsRERFJ\nO3E8R+hO4GQzmw+cFL3H3WcCzwKzgNeBK929ODrmCsKE60JgAfBa1P4g0MLMCoHrie5Aq1VOPjnc\nSj9+fNyViIiIpB2rbQMolZWXl+cFBQVxl7FLUVFYlX7YMBg1Ku5qRETKZWafuHte3HWIVDU9WTpu\nWVlw4onw5pthZEhERESqjYJQTXDyybBwoRZiFRERqWYKQjXBKaeE13Hj4q1DREQkzSgI1QSdO4dl\nNxSEREREqpWCUE1gFi6PjR8fJk+LiIhItVAQqilOPhk2boTJk+OuREREJG0oCNUUAweGkSFdHhMR\nEak2CkI1RYsWcNRR8N57cVciIiKSNhSEapL8fJg0CUpK4q5EREQkLSgI1ST5+bBpE8ydG3clIiIi\naUFBqCbJzw+vEyfu3j5jBnz6afXXIyIikuIUhGqSww6Dpk2/GYQuuAAuvDCemkRERFJYVtwFSIKM\nDDjmmN2D0KJF8NlnkJkJ27dDdnZ89YmIiKQYjQjVNPn5MG0abNkS3o8dG16Li2H27PjqEhERSUEK\nQjVNfn4IPaVzgsaMgUaNwvb06fHVJSIikoIUhGqaxAnTmzfD22/DRRdB3boKQiIiIlVMc4Rqmtat\noWPHEIQ6doQdO+AHP4AJExSEREREqpiCUE2Unw8ffQT160OzZnDssdCjRxgdEhERkSqjS2M1UX5+\nuFvshRfgtNMgKysEoaVLYf36uKsTERFJGQpCNVHpPKGvvoIzzgjbRx4ZXnV5TEREpMooCNVEvXqF\nUaCsLDj11NDWo0d4VRASERGpMpojVBPVrw/9+0OTJuFJ0wBt24b5QgpCIiIiVUZBqKYaMyY8TbqU\nWRgVUhASERGpMro0VlM1bbrrQYqlevQIC7C6x1OTiIhIilEQqk169IBNm8IdZSIiIlJpCkK1SemE\n6WnT4q1DREQkRSgI1Sbdu4dXzRMSERGpEgpCtUmTJmHZDQUhERGRKqEgVNv06KFLYyIiIlVEQai2\n6dsXZs2CtWvjrkRERKTWUxCqbQYMCK/vvRdvHSIiIikgaUHIzOqZ2SQzm2pmM83sN1F7czMbZ2bz\no9dmCcfcbGaFZjbXzE5NaO9tZtOjz0aamUXt2Wb2TNQ+0cw6Jqs/NcYxx4QnT0+YEHclIiIitV4y\nR4S2A991957AUcAgM+sL3AS85e5dgLei95hZV2AY0A0YBNxnZqWPVr4fuAToEv0MitpHAOvdvTNw\nD3BXEvtTM9StC/36wTvvxF2JiIhIrZe0IOTBV9HbOtGPA2cCj0btjwJDo+0zgafdfbu7fwEUAn3M\nrA3QxN0/dncHHitzTOm5ngMGlo4WpbQBA2DqVFi/Pu5KREREarWkzhEys0wz+wxYBYxz94lAjrsv\nj3ZZAeRE27nA4oTDl0RtudF22fbdjnH3ImAj0CIJXalZBgwIy2y8/37clYiIiNRqSQ1C7l7s7kcB\n7QijO93LfO6EUaKkMrNLzazAzApWr16d7K9Lvvx8yM7e8zyhDz+EU0+FLVuqty4REZFaplruGnP3\nDcDbhLk9K6PLXUSvq6LdlgLtEw5rF7UtjbbLtu92jJllAU2Bb9xX7u4PuHueu+e1atWqqroVn3r1\nQhja0zyhBx6AN9+E116r1rJERERqm2TeNdbKzA6ItusDJwNzgDHA8Gi34cBL0fYYYFh0J1gnwqTo\nSdFltE1m1jea/3NhmWNKz3U2MD4aZUp9AwbAlCmwcePu7SUl8PrrYfu556q/LhERkVokmSNCbYC3\nzWwaMJkwR2gscCdwspnNB06K3uPuM4FngVnA68CV7l4cnesKYDRhAvUCoHSo40GghZkVAtcT3YGW\nFk44IYSeDz7YvX3KFFi5Eg48EMaOha1bYylPRESkNshK1ondfRrQq5z2tcDAPRxzO3B7Oe0FQPdy\n2rcB51S62Nqob1+oUyfMEzrttF3tpZfD7r4bzj8/XCI788x4ahQREanh9GTp2qpBA+jT55vzhF59\nNTx08dxzoXlzXR4TERH5FgpCtdmQITBpEnz0UXi/di1MnBhGiOrUgaFDYcwY2L493jpFRERqKAWh\n2uzqq6FNG/jpT8N8oTffDK+DB4fPzz4bNm2CcePirVNERKSGUhCqzRo1gjvuCKNATz4Z5ge1bAl5\neeHzgQPhgAN0eUxERGQPkjZZWqrJBRfAX/8KN90ULoGdeipkRku01a0bJkq/9BLs2BHei4iIyH9p\nRKi2y8iAe++FpUthzZrd7yCDEIQ2bIDJk+OpT0REpAZTEEoFxx4LP/whZGXBKafs/lm/fuF14sTq\nr0tERKSGq1AQMrNDzCw72j7BzK4pfWq01BCjR8PHH4c5QokOPBA6dAh3l4mIiMhuKjoi9DxQbGad\ngQcI63s9mbSqZN81agS9e5f/WX6+RoRERETKUdEgVOLuRcD3gb+4+88IS2hIbZCfDwsXwqpVe91V\nREQknVQ0CO00s/MIC5yOjdrqJKckqXL5+eFVo0IiIiK7qWgQ+jHQD7jd3b+IVof/Z/LKkip19NHh\nlnoFIRERkd1U6DlC7j4LuAbAzJoBjd39rmQWJlWoQQPo0eObQcgdzOKpSUREpAao6F1j75hZEzNr\nDnwK/MPM/pTc0qRK5eeHZwmVlIT306ZB27bwzDPx1iUiIhKjil4aa+rum4AfAI+5ez5wUvLKkiqX\nnw8bN8K8eeH9LbfAihVw0UUwa1a8tYmIiMSkokEoy8zaAOeya7K01CaJE6Y//BBeeQWuuSbcdn/W\nWbB5c7z1iYiIxKCiQeh/gTeABe4+2cwOBuYnryypcocdBo0bhyB0662QkwO/+x08/XQYJbrkkjBn\nSEREJI1UdLL0v4B/Jbz/HDgrWUVJEmRmwjHHhFXqN26EkSOhYUM48US4/Xa4+WY47ji46qq4KxUR\nEak2FZ0s3c7M/m1mq6Kf582sXbKLkypWOk+oQwe49NJd7TfeCEOGwA03wJQp8dUnIiJSzSp6aexh\nYAzQNvp5OWqT2uTYY8PrL38J2dm72jMy4JFHoFWrsHir5guJiEiaqGgQauXuD7t7UfTzCNAqiXVJ\nMgweDG+/He4UK6tly3DZbMECuOIKzRcSEZG0UNEgtNbMzjezzOjnfGBtMguTJMjIgBNO2PNDFI8/\nHn71K3j8cXjxxWotTUREJA4VDUIXEW6dXwEsB84G/idJNUmcbr0VmjWD116LuxIREZGkq1AQcvcv\n3f0Md2/l7q3dfSi6ayw1ZWZCXh588knclYiIiCRdRUeEynN9lVUhNUvv3jB9OmzbFnclIiIiSVWZ\nIKTVOlNVXh7s3BnCkIiISAqrTBDSbUWpKi8vvBYUxFuHiIhIkn3rk6XNbDPlBx4D6ielIolfhw7h\ndnoFIRERSXHfGoTcvXF1FSI1iFkYFVIQEhGRFFeZS2OSyvLyYOZM2Lo17kpERESSRkFIypeXB8XF\nMHVq3JWIiIgkjYKQlK937/Cqy2MiIpLCkhaEzKy9mb1tZrPMbKaZXRu1NzezcWY2P3ptlnDMzWZW\naGZzzezUhPbeZjY9+mykWVgjwsyyzeyZqH2imXVMVn/STm4u5OQoCImISEpL5ohQEXCDu3cF+gJX\nmllX4CbgLXfvArwVvSf6bBjQDRgE3GdmmdG57gcuAbpEP4Oi9hHAenfvDNwD3JXE/qQXTZgWEZE0\nkLQg5O7L3f3TaHszMBvIBc4EHo12exQYGm2fCTzt7tvd/QugEOhjZm2AJu7+sbs78FiZY0rP9Rww\nsHS0SKpAXh7Mng1ffx13JSIiIklRLXOEoktWvYCJQI67L48+WgHkRNu5wOKEw5ZEbbnRdtn23Y5x\n9yJgI9CinO+/1MwKzKxg9erVVdCjNJGXByUl8NlncVciIiKSFEkPQmbWCHgeuM7dNyV+Fo3wJP0J\n1e7+gLvnuXteq1atkv11qaN0wvTkyfHWISIikiRJDUJmVocQgp5w9xei5pXR5S6i11VR+1KgfcLh\n7aK2pdF22fbdjjGzLKApsLbqe5Km2rSBtm3h00/jrkRERCQpknnXmAEPArPd/U8JH40Bhkfbw4GX\nEtqHRXeCdSJMip4UXUbbZGZ9o3NeWOaY0nOdDYyPRpmkqvTqBVOmxF2FiIhIUnzrEhuVdCxwATDd\nzEonmdwC3Ak8a2YjgC+BcwHcfaaZPQvMItxxdqW7F0fHXQE8Qljf7LXoB0LQ+qeZFQLrCHedSVXq\n1Qtefz08Ybq+lpcTEZHUkrQg5O7vExZnLc/APRxzO3B7Oe0FQPdy2rcB51SiTNmbo48OT5iePh36\n9Im7GhERkSqlJ0vLt+vVK7zq8piIiKQgBSH5dgcdBM2aacK0iIikJAUh+XZmmjAtIiIpS0FI9q5X\nL5g2DXb/YqgxAAAUTUlEQVTujLsSERGRKqUgJHt39NGwfTvMmRN3JSIiIlVKQUj2rnTCtOYJiYhI\nilEQkr079FBo0GD3eULvvQf/+Q/o+ZUiIlKLKQjJ3mVmQs+eu0aE5s+HU06Bk0+G7t1h9GjYti3e\nGkVERPaDgpBUTK9eYRX6oiIYMQLq1YP77oO6deGSS+C88+KuUEREZJ8pCEnFHH00bN4MN9wQLovd\ncw/85CdhlOiqq+CVV8LnIiIitYiCkFRM6YTpkSNh8GAYHq11awZnnx1urX/rrfjqExER2Q8KQlIx\n3bpBVhY0bgyjRoUAVKp/f2jSBF59Nb76RERE9kMyV5+XVJKdDf/7vyEQtW+/+2d16oTJ06++Gu4i\nsz2ttSsiIlKzaERIKu7mm+GMM8r/7LTTYOnSsEq9iIhILaEgJFVj0KDwqstjIiJSiygISdVo0ybc\nWaYgJCIitYiCkFSd006DDz+E9evjrkRERKRCFISk6px2GhQXw7hxcVciIiJSIQpCUnX69IHmzeHR\nR2HuXK1DJiIiNZ6CkFSdzMzwoMVXX4XDD4eWLeGKK8IokYiISA2kICRV6+67YeZM+Mc/wqKs998P\nv/lN3FWJiIiUSw9UlKqVkQFdu4afESOgQQO47TY45hg4/fS4qxMREdmNRoQkeczgb38Lt9VfcAEU\nFsZdkYiIyG4UhCS56teH558P84f694cBA8Iirb/6lSZTi4hI7BSEJPk6doSxY0MIAvjss7Bu2X/+\nE2tZIiIimiMk1aNfP/jXv8L29u3QoQOMHBkmVIuIiMREI0JS/bKz4bLL4JVXYMGCuKsREZE0piAk\n8bj88jBv6G9/i7sSERFJYwpCEo+2bcOk6Ycegq++irsaERFJUwpCEp9rroGNG+Gf/4y7EhERSVMK\nQhKfvn0hLw/+8hfdSi8iIrFIWhAys4fMbJWZzUhoa25m48xsfvTaLOGzm82s0MzmmtmpCe29zWx6\n9NlIM7OoPdvMnonaJ5pZx2T1RZLEDK6+GmbPhnfeibsaERFJQ8kcEXoEGFSm7SbgLXfvArwVvcfM\nugLDgG7RMfeZWWZ0zP3AJUCX6Kf0nCOA9e7eGbgHuCtpPZHkOeccOOAAGD067kpERCQNJS0Iufu7\nwLoyzWcCj0bbjwJDE9qfdvft7v4FUAj0MbM2QBN3/9jdHXiszDGl53oOGFg6WiS1SP36cP754enT\n68r+5yIiIpJc1T1HKMfdl0fbK4CcaDsXWJyw35KoLTfaLtu+2zHuXgRsBFqU96VmdqmZFZhZwerV\nq6uiH1KVLr44PGTx8cfjrkRERNJMbJOloxGeapkh6+4PuHueu+e1atWqOr5S9kXPnmF1+n/8Y/dJ\n019/HV9NIiKSFqo7CK2MLncRva6K2pcC7RP2axe1LY22y7bvdoyZZQFNgbVJq1yS65JLYMYMmDQJ\nSkrgF7+Axo3h/vvjrkxERFJYdQehMcDwaHs48FJC+7DoTrBOhEnRk6LLaJvMrG80/+fCMseUnuts\nYHw0yiS10bBh0LAh3Hsv/OAHcPvtkJsLV10Fr70Wd3UiIpKiknn7/FPAR8BhZrbEzEYAdwInm9l8\n4KToPe4+E3gWmAW8Dlzp7sXRqa4ARhMmUC8ASv9WfBBoYWaFwPVEd6BJLdW4cQhDTz8dVqofOTLc\nVt+zJ5x7LkydCsuWwfXXQ8uWMHx4eBijiIhIJVi6DaLk5eV5QUFB3GVIeWbNghEj4Lbb4KSTQtvS\npZCfD9u2haU4iopg4EB46y1o1w4eewyOPz7eukXSgJl94u55cdchUtX0ZGmpObp2hY8+2hWCIFwe\ne+WV8Kyh4cNh3jx44w14/32oUwdOOAGuvRY2bYqtbBERqb0UhKTm69kTCgth1Cg4+ODQ1rcvTJkC\nV1wRlug44gh47jkt1SEiIvtEQUhqr0aN4K9/hY8/hpyc8JTqyy5TGBIRkQpTEJLar0+fcNv9z38e\nnkX0s58pDImISIVkxV2ASJXIyoI77ggPYfzjH6FZM7j11rirEhGRGk5BSFKHGfz5z7BhQ3ggY5Mm\nYXV7ERGRPVAQktSSkQEPPQSbN8M114Tb7X/607irEhGRGkpzhCT11KkDzz4LZ58dHsB4xx1xVyQi\nIjWURoQkNdWtC089FV5vuSXMIfrZz+KuSkREahiNCEnqysoKT54+66wwZ2jBgrgrEhGRGkZBSFJb\nZmZ44GLdunDDDXFXIyIiNYyCkKS+Nm3CrfQvvQTjxsVdjYiI1CAKQpIerrsuLM9x3XXhTjIREREU\nhCRd1KsXHrQ4a1a4VLY3ejK1iEhaUBCS9HHmmXDqqeGW+gsvhNWry9/v17+Gzp1h2bJqLU9ERKqf\ngpCkDzP497/DfKGnn4bDDgsPX0wc/Rk9Gn7zG/j8c7joIo0MiYikOAUhSS/168NvfwtTp0KPHjBi\nBAwaBIsWhYnUl18e3o8cCW+8AffdF3fFIiKSROZp9i/evLw8LygoiLsMqQlKSmDUqPCgxYzo3wQd\nO8L770PjxnDaaTBhAnz6afjs73+HDz+Ehx+Gbt1iK1skDmb2ibvnxV2HSFVTEBL54gu49FKYPx/e\new/atw/ty5eHUaPi4rCQa5060LBhGFV67z045JB46xapRgpCkqp0aUykU6dwWezzz3eFIAjPH3rk\nkTBKdPvtsHhxGC3asQNOOgmWLAn7rV0bRo2Ki+OoXkREKkEjQiL76pNP4MQT4YADIDsbCgtD+7Bh\nYUmPOnXirU8kCTQiJKlKi66K7KveveGVV8KSHbm5YcL15s3wu9/Bzp3w5JNhSQ8REanxFIRE9sd3\nvgOTJu3e1rp1eHL12WfDj34E69eHgHTaaZpcLSJSQykIiVSVa68Nl8WuvBJefnlX+003wSWXhOcT\ntWoFs2eHy2v9+kGXLvHVKyIimiMkUuU+/xy2boVmzcL7u+4KzyOqVw+yssIdaABNm8Lrr0PfvvHV\nKlJBmiMkqUpBSKQ6zJsHd9wR5g717x9GgoYPhxUrwnyj44+Pu0KRb6UgJKlKl8ZEqsOhh4YHMSaa\nMCHchj9oENxyC7RtCy1awNFH734bv4iIJI2CkEhc2rYNYWjIEPh//29Xe3Y23HhjmFvUoMG3n6O4\nGL76KlxmS1RSAlOmhPlIc+eGh0Oedx5897thzTUREQF0aUwkfu4hzKxbB6tWwb33hlvwDzoo3KJ/\n0EFw4IEhIK1cGX4KC8NyHxMnhjvT+vWDs84Ko0kvvwzPPAPLloXzZ2SEJ2Jv3gwDBsBtt8Fxx+0e\niNzhyy/D99SrF8/vQWo0XRqTVKUgJFITTZgAV10FM2aU/3lGBhx5ZJhv1LIljBkDn30WPqtbFwYP\nhnPOCcHokEPCCNEDD4R5SitWhKdmf+c70KdPGDV6883w5OxmzcLcpcsug8MPr1wfiotDGCssDKNS\nc+fC6tVwxBGh9qOPDs9hSrRyJbzwQqhl1aoQDg85JNTZp0+4ZJihB+LHQUFIUpWCkEhNVVISgsTK\nlSG8bNsWnlWUkxMCRMOGu++/YAFMmwYnnLDrjrWytmwJo01vvw3vvhuWCWnaFAYODKNFH3wA//53\neDBk377wwx+G5yK1a7frHO7hzrh33w311asXRqu2bAnrtn3xRfh84cJwnlL164c5UKVLkwB07w6n\nnx4eUvmvf4UQtHNnuLuuVatQ2+efh2VNIISgFi3CZy1aQPPm4WfnTlizJix30qABHHZYmJfVrFm4\ng2/LlnDMkCHhNbEfS5ZAr17QpMnuv6tt20J4mzkTFi0KS7F06xbOW5kHZrrDxo3he3fuDPU3axYW\n+i172XLjxhAIc3P3fpk0yRSEJFXV+iBkZoOAPwOZwGh3v/Pb9lcQEom4h5DVsmUIHqVWrQprrD31\n1K5RpnbtQihp2jRcQlu6tPxzNm8eAkOnTnDwwbt+DjssnCMjAzZtgunT4eOPYezYsIBtcXFYsuR/\n/icsgHv44btCwY4dIeAVFITvXb06/KxbF4LPunUhmLRoEX42bw536a1Z8836MjLCSFjHjvDOO6Ev\nEL6ra9dQ5/LlIfgsWxZ+R2VlZob5Xe3ahdfMzBBoiovD76d16xC2WrbcFXIWLgz9nTQpLO771Vff\nPG/DhtC5c7ijMDMzrF83f/7uv9sOHUIQO+yw8Dvq1i28ZmdDUVEYSVu4MPx+1qwJj2po0SLUmZsb\nRtfKzierIAUhSVW1OgiZWSYwDzgZWAJMBs5z91l7OkZBSGQfzJsXRmoWLAijExs2hL/kBwwIt/x3\n7hyCyrZtIYzsz1+y69eHwJWfX7WjHuvWhVBUv374KSyEF18MI17Ll4f6Bw4MoeiTT+Cjj8IIUW5u\nCBwHHRQu43XrFt5/8UUYHZo9OwSOJUt2haWsrBBeNmwIQXLr1m/W06RJ6GPXruESX/v24QGc69eH\nWpcuDcFn/vzwO+3VK4yUtWsXvmfx4lDDvHkh7JSUhPOWBrPly0MY+jZ//Wt44Od+UBCSVFXbg1A/\n4Nfufmr0/mYAd79jT8coCIlI0pVOfi8dsWrTJozcVNX8pu3bQ2CaMSP8fPllCEyHHBJG43JywohU\n06ahhmXLQtDq2TOM0O0HBSFJVbX99vlcYHHC+yVAftmdzOxS4FKADh06VE9lIpK+GjUKP8n68yY7\nO8yv6t597/u2a7f7HC8R2U1a3H7h7g+4e56757UqnSgpIiIiaa+2B6GlQOIjeNtFbSIiIiJ7VduD\n0GSgi5l1MrO6wDBgTMw1iYiISC1Rq+cIuXuRmV0FvEG4ff4hd58Zc1kiIiJSS9TqIATg7q8Cr8Zd\nh4iIiNQ+tf3SmIiIiMh+UxASERGRtKUgJCIiImmrVj9Zen+Y2Wrgy/08vCVQzgJGKS8d+52OfYb0\n7Hc69hn2vd8HubsexCYpJ+2CUGWYWUE6PmI+Hfudjn2G9Ox3OvYZ0rffImXp0piIiIikLQUhERER\nSVsKQvvmgbgLiEk69jsd+wzp2e907DOkb79FdqM5QiIiIpK2NCIkIiIiaUtBSERERNKWglAFmdkg\nM5trZoVmdlPc9SSDmbU3s7fNbJaZzTSza6P25mY2zszmR6/N4q61qplZpplNMbOx0ft06PMBZvac\nmc0xs9lm1i/V+21mP43+255hZk+ZWb1U7LOZPWRmq8xsRkLbHvtpZjdHf7bNNbNT46laJB4KQhVg\nZpnA34DBQFfgPDPrGm9VSVEE3ODuXYG+wJVRP28C3nL3LsBb0ftUcy0wO+F9OvT5z8Dr7n440JPQ\n/5Ttt5nlAtcAee7eHcgEhpGafX4EGFSmrdx+Rv8fHwZ0i465L/ozTyQtKAhVTB+g0N0/d/cdwNPA\nmTHXVOXcfbm7fxptbyb8xZhL6Ouj0W6PAkPjqTA5zKwdMAQYndCc6n1uChwPPAjg7jvcfQMp3m8g\nC6hvZllAA2AZKdhnd38XWFemeU/9PBN42t23u/sXQCHhzzyRtKAgVDG5wOKE90uitpRlZh2BXsBE\nIMfdl0cfrQByYiorWe4FbgRKEtpSvc+dgNXAw9ElwdFm1pAU7re7LwXuBhYBy4GN7v4mKdznMvbU\nz7T7800kkYKQfIOZNQKeB65z902Jn3l43kLKPHPBzL4HrHL3T/a0T6r1OZIFHA3c7+69gK8pc0ko\n1fodzYk5kxAC2wINzez8xH1Src97ki79FKkIBaGKWQq0T3jfLmpLOWZWhxCCnnD3F6LmlWbWJvq8\nDbAqrvqS4FjgDDNbSLjk+V0ze5zU7jOEf/UvcfeJ0fvnCMEolft9EvCFu692953AC0B/UrvPifbU\nz7T5802kPApCFTMZ6GJmncysLmFi4ZiYa6pyZmaEOSOz3f1PCR+NAYZH28OBl6q7tmRx95vdvZ27\ndyT87zre3c8nhfsM4O4rgMVmdljUNBCYRWr3exHQ18waRP+tDyTMg0vlPifaUz/HAMPMLNvMOgFd\ngEkx1CcSCz1ZuoLM7DTCXJJM4CF3vz3mkqqcmR0HvAdMZ9d8mVsI84SeBToAXwLnunvZiZi1npmd\nAPxfd/+embUgxftsZkcRJojXBT4Hfkz4x1HK9tvMfgP8kHCH5BTgYqARKdZnM3sKOAFoCawEfgW8\nyB76aWa3AhcRfi/XuftrMZQtEgsFIREREUlbujQmIiIiaUtBSERERNKWgpCIiIikLQUhERERSVsK\nQiIiIpK2FIREKsHMis3sMzObamafmln/vex/gJldUYHzvmNmeVVXqYiIlEdBSKRytrr7Ue7eE7gZ\nuGMv+x8A7DUIiYhI9VAQEqk6TYD1ENZrM7O3olGi6WZ2ZrTPncAh0SjSH6J9fx7tM9XM7kw43zlm\nNsnM5pnZd6J9M83sD2Y22cymmdllUXsbM3s3Ou+M0v1FROTbZcVdgEgtV9/MPgPqAW2A70bt24Dv\nu/smM2sJfGxmYwgLm3Z396MAzGwwYSHQfHffYmbNE86d5e59oqea/4qwVtYIwqrpx5hZNvCBmb0J\n/AB4w91vN7NMoEHSey4ikgIUhEQqZ2tCqOkHPGZm3QEDfmdmxxOWK8kFcso5/iTgYXffAlBmaYfS\nRW8/ATpG26cAR5rZ2dH7poS1oSYDD0WL5r7o7p9VUf9ERFKagpBIFXH3j6LRn1bAadFrb3ffGa1u\nX28fT7k9ei1m1/9XDbja3d8ou3MUuoYAj5jZn9z9sf3ohohIWtEcIZEqYmaHExblXUsYqVkVhaAT\ngYOi3TYDjRMOGwf82MwaROdIvDRWnjeAn0QjP5jZoWbW0MwOAla6+z8IC6keXVX9EhFJZRoREqmc\n0jlCEEZrhrt7sZk9AbxsZtOBAmAOgLuvNbMPzGwG8Jq7/yxaBb7AzHYArwK3fMv3jSZcJvvUzAxY\nDQwlrDT+MzPbCXwFXFjVHRURSUVafV5ERETSli6NiYiISNpSEBIREZG0pSAkIiIiaUtBSERERNKW\ngpCIiIikLQUhERERSVsKQiIiIpK2/j9A/QIr+YfY4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc72668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   77.840% -- tf.random_uniform [0, 1)\n",
      "Loss\n",
      "    8.684  -- tf.random_uniform [0, 1)\n"
     ]
    }
   ],
   "source": [
    "# Default for tf.random_uniform is minval=0 and maxval=1\n",
    "basline_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Baseline',\n",
    "    [(basline_weights, 'tf.random_uniform [0, 1)')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss graph is showing the neural network is learning, which it didn't with all zeros or all ones. We're headed in the right direction.\n",
    "\n",
    "### General rule for setting weights\n",
    "The general rule for setting the weights in a neural network is to be close to zero without being too small. A good pracitce is to start your weights in the range of $[-y, y]$ where\n",
    "$y=1/\\sqrt{n}$ ($n$ is the number of inputs to a given neuron).\n",
    "\n",
    "Let's see if this holds true, let's first center our range over zero.  This will give us the range [-1, 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "损失图展示了没有使用全0或者全1weights的神经网络正在学习。我们正朝着正确的方向前进"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkUAAAEWCAYAAABojOMFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOApgHJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12238b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   70.240% -- tf.random_uniform [0, 1)\n",
      "   91.180% -- tf.random_uniform [-1, 1)\n",
      "Loss\n",
      "   19.605  -- tf.random_uniform [0, 1)\n",
      "    2.422  -- tf.random_uniform [-1, 1)\n"
     ]
    }
   ],
   "source": [
    "uniform_neg1to1_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -1, 1)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -1, 1)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -1, 1))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    '[0, 1) vs [-1, 1)',\n",
    "    [\n",
    "        (basline_weights, 'tf.random_uniform [0, 1)'),\n",
    "        (uniform_neg1to1_weights, 'tf.random_uniform [-1, 1)')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going in the right direction, the accuracy and loss is better with [-1, 1). We still want smaller weights. How far can we go before it's too small?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们正朝着正确的方向，精度和损失函数都比[-1,1)好。我们仍然需要更小的weights，在太小之前我们还可以走多远。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Too small\n",
    "Let's compare [-0.1, 0.1), [-0.01, 0.01), and [-0.001, 0.001) to see how small is too small.  We'll also set `plot_n_batches=None` to show all the batches in the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 太小\n",
    "\n",
    "让我们对比[-0.1,0.1),[-0.01,0.01)和[-0.001,0.001)看多小才是更小。我们设置 plot_n_batches=None 展示所有的batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAEWCAYAAAD2NuSlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhJQUiIiICKCkQEREREL/HyhqVlgu/uxDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124ade828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   90.780% -- [-1, 1)\n",
      "   96.660% -- [-0.1, 0.1)\n",
      "   95.240% -- [-0.01, 0.01)\n",
      "   94.040% -- [-0.001, 0.001)\n",
      "Loss\n",
      "    6.109  -- [-1, 1)\n",
      "    0.117  -- [-0.1, 0.1)\n",
      "    0.215  -- [-0.01, 0.01)\n",
      "    0.197  -- [-0.001, 0.001)\n"
     ]
    }
   ],
   "source": [
    "uniform_neg01to01_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -0.1, 0.1)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -0.1, 0.1)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -0.1, 0.1))\n",
    "]\n",
    "\n",
    "uniform_neg001to001_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -0.01, 0.01)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -0.01, 0.01)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -0.01, 0.01))\n",
    "]\n",
    "\n",
    "uniform_neg0001to0001_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -0.001, 0.001)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -0.001, 0.001)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -0.001, 0.001))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    '[-1, 1) vs [-0.1, 0.1) vs [-0.01, 0.01) vs [-0.001, 0.001)',\n",
    "    [\n",
    "        (uniform_neg1to1_weights, '[-1, 1)'),\n",
    "        (uniform_neg01to01_weights, '[-0.1, 0.1)'),\n",
    "        (uniform_neg001to001_weights, '[-0.01, 0.01)'),\n",
    "        (uniform_neg0001to0001_weights, '[-0.001, 0.001)')],\n",
    "    plot_n_batches=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like anything [-0.01, 0.01) or smaller is too small.  Let's compare this to our typical rule of using the range $y=1/\\sqrt{n}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来像[-0.01,0.01)或者更小的属于太小。让我们拿经典的规则(范围在 $y = 1 / \\sqrt{n}$)和它们对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAGHCAYAAACQ38U0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d311e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   96.880% -- [-0.1, 0.1)\n",
      "   96.900% -- General Rule\n",
      "Loss\n",
      "    0.169  -- [-0.1, 0.1)\n",
      "    0.088  -- General Rule\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "general_rule_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -1/np.sqrt(layer_1_weight_shape[0]), 1/np.sqrt(layer_1_weight_shape[0]))),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -1/np.sqrt(layer_2_weight_shape[0]), 1/np.sqrt(layer_2_weight_shape[0]))),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -1/np.sqrt(layer_3_weight_shape[0]), 1/np.sqrt(layer_3_weight_shape[0])))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    '[-0.1, 0.1) vs General Rule',\n",
    "    [\n",
    "        (uniform_neg01to01_weights, '[-0.1, 0.1)'),\n",
    "        (general_rule_weights, 'General Rule')],\n",
    "    plot_n_batches=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The range we found and $y=1/\\sqrt{n}$ are really close.\n",
    "\n",
    "Since the uniform distribution has the same chance to pick anything in the range, what if we used a distribution that had a higher chance of picking numbers closer to 0.  Let's look at the normal distribution.\n",
    "### Normal Distribution\n",
    "Unlike the uniform distribution, the [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution) has a higher likelihood of picking number close to it's mean. To visualize it, let's plot values from TensorFlow's `tf.random_normal` function to a histogram.\n",
    "\n",
    ">[tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)](https://www.tensorflow.org/api_docs/python/tf/random_normal)\n",
    "\n",
    ">Outputs random values from a normal distribution.\n",
    "\n",
    ">- **shape:** A 1-D integer Tensor or Python array. The shape of the output tensor.\n",
    "- **mean:** A 0-D Tensor or Python value of type dtype. The mean of the normal distribution.\n",
    "- **stddev:** A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution.\n",
    "- **dtype:** The type of the output.\n",
    "- **seed:** A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.\n",
    "- **name:** A name for the operation (optional)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现的范围和 $y = 1 / \\sqrt{n}$ 十分接近\n",
    "因为均匀分布有相同的几率从这个区间中获取任何数字，如果我们使用一个分布，有用更好的几率获取靠近0的数字呢？让我们看看正态分布。\n",
    "\n",
    "## Normal Distribution 正态分布\n",
    "和均匀分布不同，正态分布有更高的几率获取靠近其均值的数字。我们画出 `tr.random_normal` 生成的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFyCAYAAACDemKtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH/e9mPGlRILvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cda4f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.hist_dist('Random Normal (mean=0.0, stddev=1.0)', tf.random_normal([1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the normal distribution against the previous uniform distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们拿之前的均匀分布对比下正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAGHCAYAAADsnI7EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6JjmOGnFUeIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc04a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   96.920% -- Uniform [-0.1, 0.1)\n",
      "   97.200% -- Normal stddev 0.1\n",
      "Loss\n",
      "    0.103  -- Uniform [-0.1, 0.1)\n",
      "    0.099  -- Normal stddev 0.1\n"
     ]
    }
   ],
   "source": [
    "normal_01_weights = [\n",
    "    tf.Variable(tf.random_normal(layer_1_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.random_normal(layer_2_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.random_normal(layer_3_weight_shape, stddev=0.1))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Uniform [-0.1, 0.1) vs Normal stddev 0.1',\n",
    "    [\n",
    "        (uniform_neg01to01_weights, 'Uniform [-0.1, 0.1)'),\n",
    "        (normal_01_weights, 'Normal stddev 0.1')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal distribution gave a slight increasse in accuracy and loss.  Let's move closer to 0 and drop picked numbers that are `x` number of standard deviations away.  This distribution is called [Truncated Normal Distribution](https://en.wikipedia.org/wiki/Truncated_normal_distribution%29).\n",
    "### Truncated Normal Distribution\n",
    ">[tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)](https://www.tensorflow.org/api_docs/python/tf/truncated_normal)\n",
    "\n",
    ">Outputs random values from a truncated normal distribution.\n",
    "\n",
    ">The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.\n",
    "\n",
    ">- **shape:** A 1-D integer Tensor or Python array. The shape of the output tensor.\n",
    "- **mean:** A 0-D Tensor or Python value of type dtype. The mean of the truncated normal distribution.\n",
    "- **stddev:** A 0-D Tensor or Python value of type dtype. The standard deviation of the truncated normal distribution.\n",
    "- **dtype:** The type of the output.\n",
    "- **seed:** A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.\n",
    "- **name:** A name for the operation (optional)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正态分布在精度和损耗上有一点点提升。让我们更靠近0，丢掉挑选的数字？？。这个分布成为截断正态分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFyCAYAAACDemKtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYGZmlkMOAMzMzHLIAYCZmVkO/X+vp+lxH+vC3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d35f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.hist_dist('Truncated Normal (mean=0.0, stddev=1.0)', tf.truncated_normal([1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's compare the previous results with the previous distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们和之前的结果进行对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trunc_normal_01_weights = [\n",
    "    tf.Variable(tf.truncated_normal(layer_1_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.truncated_normal(layer_2_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.truncated_normal(layer_3_weight_shape, stddev=0.1))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Normal vs Truncated Normal',\n",
    "    [\n",
    "        (normal_01_weights, 'Normal'),\n",
    "        (trunc_normal_01_weights, 'Truncated Normal')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's no difference between the two, but that's because the neural network we're using is too small. A larger neural network will pick more points on the normal distribution, increasing the likelihood it's choices are larger than 2 standard deviations.\n",
    "\n",
    "We've come a long way from the first set of weights we tested. Let's see the difference between the weights we used then and now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGHCAYAAADGLH9rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvPXtQSx8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f4d2710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   66.100% -- Baseline\n",
      "   97.040% -- Truncated Normal\n",
      "Loss\n",
      "   24.090  -- Baseline\n",
      "    0.075  -- Truncated Normal\n"
     ]
    }
   ],
   "source": [
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Baseline vs Truncated Normal',\n",
    "    [\n",
    "        (basline_weights, 'Baseline'),\n",
    "        (trunc_normal_01_weights, 'Truncated Normal')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a huge difference. You can barely see the truncated normal line.  However, this is not the end your learning path.  We've provided more resources for initializing weights in the classroom!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "真是巨大的区别，你几乎看不到截断正态的线。然后这不是你学习曲线的重点。我们提供了更多的关于初始化weights的资源在课程中"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
